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飞机襟翼使用过程中存在失效判据不易确定的问题,目前襟翼机构滑轨滑轮磨损间隙许用量主要是通过试验及使用统计数据分析确定。针对新研机构缺乏试验及使用数据的问题,通过对影响磨损间隙最大许用量的因素分析,提出应用神经网络学习确定襟翼滑轨滑轮间隙最大许用量的预测方法。算例验证表明,运用神经网络方法预测得到的滑轨滑轮架的间隙最大许用量误差小于10%,说明方法有效。该方法对当前新研机构的失效判据确定具有重要的工程意义。
The use of aircraft flaps in the process of failure criterion is not easy to determine the problem, the current flaps slide pulley wear allowance is mainly through the use of statistics and experimental analysis. Aiming at the problem of lack of test and use data of new institutes, this paper puts forward a prediction method of neural network learning to determine the maximum allowable clearance of pulley flanges of flaps by analyzing the factors affecting the maximum allowable wear gap. The case study shows that the error of the maximum allowable clearance of the sliding carriage predicted by the neural network method is less than 10%, which shows that the method is effective. The method has important engineering significance to determine the failure criterion of the new institute.